Adaptive Retrieval-Augmented Generation (RAG) for Structured Clinical Notes from Patient–Provider Transcripts: A Multilingual Study
TL;DR
Adaptive RAG can improve structure compliance and reduce hallucinations in multilingual clinical note generation without dataset-specific prompt engineering or model fine-tuning, and these findings support retrieval-based format adaptation as a generalizable mechanism for diverse clinical documentation contexts.
Abstract
Background/Objectives: Clinical documentation places a significant time burden on healthcare professionals, including in the context of home care. Large language models (LLMs) offer potential for automated note generation, but current approaches rely on static prompt templates that fail to generalize across care settings, languages, and documentation formats. This study proposes and evaluates an adaptive retrieval-augmented generation (RAG) framework that uses retrieval as a format adaptation mechanism, enabling the generation of structured clinical notes from patient–provider transcripts across various documentation formats without model fine-tuning. Methods: The proposed framework retrieves dialogue–note pairs that demonstrate the structure of specific sections, allowing the transfer of formatting knowledge during inference. Experiments were conducted on three datasets covering two languages and various documentation formats: the Japanese Visiting Nurse corpus (JP-VN), MTS-Dialog, and ACI-BENCH. Six controlled conditions were evaluated: zero-shot prompting (C1), static few-shot prompting (C2), dense retrieval (C3), random retrieval (C4), sparse BM25 retrieval (C5), and hybrid retrieval using reciprocal rank fusion (RRF) (C6). Performance metrics include structural adherence to required section headings, content quality (ROUGE-1, BLEU, BERTScore), and the number of hallucinated clinical entities per generated record. Results: Structure compliance increased from 0–37% under static conditions (C1/C2) to 91–100% under all adaptive RAG conditions (C3–C6) across all datasets. On MTS-Dialog, dense retrieval achieved the highest content quality (ROUGE-1: 0.519 vs. 0.446–0.492 for C4–C6; p<0.001). Hallucinated entities in JP-VN decreased from 2.73–3.58 per note (C1/C2) to 1.15–1.30 (C3–C6), an approximately 55–56% reduction. Conclusions: Adaptive RAG can improve structure compliance and reduce hallucinations in multilingual clinical note generation without dataset-specific prompt engineering or model fine-tuning. These findings support retrieval-based format adaptation as a generalizable mechanism for diverse clinical documentation contexts.